Users increasingly turn to computing services, such as database engines, to analyze large volumes of data. For example, Online Analytical Processing (OLAP) systems can be designed to analyze clinical-testing or business-intelligence (BI) data. However, many database systems cannot provide realtime or near-realtime query responses on large datasets. As a result, users must plan their analyses ahead of time and cannot engage in exploratory analysis to attempt to locate patterns in the data. Some “approximate query processing” (AQP) database systems reduce response delay by providing approximate answers to queries. However, answers provided by these systems may be far from the actual answer, preventing users from relying on the approximate results.